257 lines
9.3 KiB
Python
257 lines
9.3 KiB
Python
# Authors: Andreas Mueller <andreas.mueller@columbia.edu>
|
|
# Guillaume Lemaitre <guillaume.lemaitre@inria.fr>
|
|
# License: BSD 3 clause
|
|
|
|
import warnings
|
|
|
|
import numpy as np
|
|
|
|
from ..base import BaseEstimator, RegressorMixin, clone
|
|
from ..utils.validation import check_is_fitted
|
|
from ..utils import check_array, _safe_indexing
|
|
from ..preprocessing import FunctionTransformer
|
|
from ..utils.validation import _deprecate_positional_args
|
|
from ..exceptions import NotFittedError
|
|
|
|
__all__ = ['TransformedTargetRegressor']
|
|
|
|
|
|
class TransformedTargetRegressor(RegressorMixin, BaseEstimator):
|
|
"""Meta-estimator to regress on a transformed target.
|
|
|
|
Useful for applying a non-linear transformation to the target ``y`` in
|
|
regression problems. This transformation can be given as a Transformer
|
|
such as the QuantileTransformer or as a function and its inverse such as
|
|
``log`` and ``exp``.
|
|
|
|
The computation during ``fit`` is::
|
|
|
|
regressor.fit(X, func(y))
|
|
|
|
or::
|
|
|
|
regressor.fit(X, transformer.transform(y))
|
|
|
|
The computation during ``predict`` is::
|
|
|
|
inverse_func(regressor.predict(X))
|
|
|
|
or::
|
|
|
|
transformer.inverse_transform(regressor.predict(X))
|
|
|
|
Read more in the :ref:`User Guide <transformed_target_regressor>`.
|
|
|
|
.. versionadded:: 0.20
|
|
|
|
Parameters
|
|
----------
|
|
regressor : object, default=None
|
|
Regressor object such as derived from ``RegressorMixin``. This
|
|
regressor will automatically be cloned each time prior to fitting.
|
|
If regressor is ``None``, ``LinearRegression()`` is created and used.
|
|
|
|
transformer : object, default=None
|
|
Estimator object such as derived from ``TransformerMixin``. Cannot be
|
|
set at the same time as ``func`` and ``inverse_func``. If
|
|
``transformer`` is ``None`` as well as ``func`` and ``inverse_func``,
|
|
the transformer will be an identity transformer. Note that the
|
|
transformer will be cloned during fitting. Also, the transformer is
|
|
restricting ``y`` to be a numpy array.
|
|
|
|
func : function, default=None
|
|
Function to apply to ``y`` before passing to ``fit``. Cannot be set at
|
|
the same time as ``transformer``. The function needs to return a
|
|
2-dimensional array. If ``func`` is ``None``, the function used will be
|
|
the identity function.
|
|
|
|
inverse_func : function, default=None
|
|
Function to apply to the prediction of the regressor. Cannot be set at
|
|
the same time as ``transformer`` as well. The function needs to return
|
|
a 2-dimensional array. The inverse function is used to return
|
|
predictions to the same space of the original training labels.
|
|
|
|
check_inverse : bool, default=True
|
|
Whether to check that ``transform`` followed by ``inverse_transform``
|
|
or ``func`` followed by ``inverse_func`` leads to the original targets.
|
|
|
|
Attributes
|
|
----------
|
|
regressor_ : object
|
|
Fitted regressor.
|
|
|
|
transformer_ : object
|
|
Transformer used in ``fit`` and ``predict``.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.linear_model import LinearRegression
|
|
>>> from sklearn.compose import TransformedTargetRegressor
|
|
>>> tt = TransformedTargetRegressor(regressor=LinearRegression(),
|
|
... func=np.log, inverse_func=np.exp)
|
|
>>> X = np.arange(4).reshape(-1, 1)
|
|
>>> y = np.exp(2 * X).ravel()
|
|
>>> tt.fit(X, y)
|
|
TransformedTargetRegressor(...)
|
|
>>> tt.score(X, y)
|
|
1.0
|
|
>>> tt.regressor_.coef_
|
|
array([2.])
|
|
|
|
Notes
|
|
-----
|
|
Internally, the target ``y`` is always converted into a 2-dimensional array
|
|
to be used by scikit-learn transformers. At the time of prediction, the
|
|
output will be reshaped to a have the same number of dimensions as ``y``.
|
|
|
|
See :ref:`examples/compose/plot_transformed_target.py
|
|
<sphx_glr_auto_examples_compose_plot_transformed_target.py>`.
|
|
|
|
"""
|
|
@_deprecate_positional_args
|
|
def __init__(self, regressor=None, *, transformer=None,
|
|
func=None, inverse_func=None, check_inverse=True):
|
|
self.regressor = regressor
|
|
self.transformer = transformer
|
|
self.func = func
|
|
self.inverse_func = inverse_func
|
|
self.check_inverse = check_inverse
|
|
|
|
def _fit_transformer(self, y):
|
|
"""Check transformer and fit transformer.
|
|
|
|
Create the default transformer, fit it and make additional inverse
|
|
check on a subset (optional).
|
|
|
|
"""
|
|
if (self.transformer is not None and
|
|
(self.func is not None or self.inverse_func is not None)):
|
|
raise ValueError("'transformer' and functions 'func'/"
|
|
"'inverse_func' cannot both be set.")
|
|
elif self.transformer is not None:
|
|
self.transformer_ = clone(self.transformer)
|
|
else:
|
|
if self.func is not None and self.inverse_func is None:
|
|
raise ValueError("When 'func' is provided, 'inverse_func' must"
|
|
" also be provided")
|
|
self.transformer_ = FunctionTransformer(
|
|
func=self.func, inverse_func=self.inverse_func, validate=True,
|
|
check_inverse=self.check_inverse)
|
|
# XXX: sample_weight is not currently passed to the
|
|
# transformer. However, if transformer starts using sample_weight, the
|
|
# code should be modified accordingly. At the time to consider the
|
|
# sample_prop feature, it is also a good use case to be considered.
|
|
self.transformer_.fit(y)
|
|
if self.check_inverse:
|
|
idx_selected = slice(None, None, max(1, y.shape[0] // 10))
|
|
y_sel = _safe_indexing(y, idx_selected)
|
|
y_sel_t = self.transformer_.transform(y_sel)
|
|
if not np.allclose(y_sel,
|
|
self.transformer_.inverse_transform(y_sel_t)):
|
|
warnings.warn("The provided functions or transformer are"
|
|
" not strictly inverse of each other. If"
|
|
" you are sure you want to proceed regardless"
|
|
", set 'check_inverse=False'", UserWarning)
|
|
|
|
def fit(self, X, y, **fit_params):
|
|
"""Fit the model according to the given training data.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
|
Training vector, where n_samples is the number of samples and
|
|
n_features is the number of features.
|
|
|
|
y : array-like of shape (n_samples,)
|
|
Target values.
|
|
|
|
**fit_params : dict
|
|
Parameters passed to the ``fit`` method of the underlying
|
|
regressor.
|
|
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
"""
|
|
y = check_array(y, accept_sparse=False, force_all_finite=True,
|
|
ensure_2d=False, dtype='numeric')
|
|
|
|
# store the number of dimension of the target to predict an array of
|
|
# similar shape at predict
|
|
self._training_dim = y.ndim
|
|
|
|
# transformers are designed to modify X which is 2d dimensional, we
|
|
# need to modify y accordingly.
|
|
if y.ndim == 1:
|
|
y_2d = y.reshape(-1, 1)
|
|
else:
|
|
y_2d = y
|
|
self._fit_transformer(y_2d)
|
|
|
|
# transform y and convert back to 1d array if needed
|
|
y_trans = self.transformer_.transform(y_2d)
|
|
# FIXME: a FunctionTransformer can return a 1D array even when validate
|
|
# is set to True. Therefore, we need to check the number of dimension
|
|
# first.
|
|
if y_trans.ndim == 2 and y_trans.shape[1] == 1:
|
|
y_trans = y_trans.squeeze(axis=1)
|
|
|
|
if self.regressor is None:
|
|
from ..linear_model import LinearRegression
|
|
self.regressor_ = LinearRegression()
|
|
else:
|
|
self.regressor_ = clone(self.regressor)
|
|
|
|
self.regressor_.fit(X, y_trans, **fit_params)
|
|
|
|
return self
|
|
|
|
def predict(self, X):
|
|
"""Predict using the base regressor, applying inverse.
|
|
|
|
The regressor is used to predict and the ``inverse_func`` or
|
|
``inverse_transform`` is applied before returning the prediction.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
|
Samples.
|
|
|
|
Returns
|
|
-------
|
|
y_hat : ndarray of shape (n_samples,)
|
|
Predicted values.
|
|
|
|
"""
|
|
check_is_fitted(self)
|
|
pred = self.regressor_.predict(X)
|
|
if pred.ndim == 1:
|
|
pred_trans = self.transformer_.inverse_transform(
|
|
pred.reshape(-1, 1))
|
|
else:
|
|
pred_trans = self.transformer_.inverse_transform(pred)
|
|
if (self._training_dim == 1 and
|
|
pred_trans.ndim == 2 and pred_trans.shape[1] == 1):
|
|
pred_trans = pred_trans.squeeze(axis=1)
|
|
|
|
return pred_trans
|
|
|
|
def _more_tags(self):
|
|
return {'poor_score': True, 'no_validation': True}
|
|
|
|
@property
|
|
def n_features_in_(self):
|
|
# For consistency with other estimators we raise a AttributeError so
|
|
# that hasattr() returns False the estimator isn't fitted.
|
|
try:
|
|
check_is_fitted(self)
|
|
except NotFittedError as nfe:
|
|
raise AttributeError(
|
|
"{} object has no n_features_in_ attribute."
|
|
.format(self.__class__.__name__)
|
|
) from nfe
|
|
|
|
return self.regressor_.n_features_in_
|